Biology subjects

Alm, E. J.

Publications and source records attributed to Alm, E. J..

34 records · Page 2Linked to original sources

bioRxiv2019

Choosing Your Battles: Which Resistance Genes Warrant Global Action?

The increasing accumulation of antibiotic resistance genes (ARGs) in pathogens poses a severe threat to the treatment of bacterial infections. However, not all ARGs do not pose the same threats to human health. Here, we present a framework to rank the risk of ARGs based on three factors: \"anthropogenic enrichment\", \"mobility\", and \"host pathogenicity\". The framework is informed by all available bacterial genomes (55,000), plasmids (16,000), integrons (3,000), and 850 metagenomes covering diverse global eco-habitats. The framework prioritizes 3% of all known ARGs in Rank I (the most at risk of dissemination amongst pathogens) and 0.3% of ARGs in Rank II (high potential emergence of new resistance in pathogens). We further validated the framework using a list of 38 ARG families previously identified as high risk by the World Health Organization and published literature, and found that 36 of them were properly identified as top risk (Rank I) in our approach. Furthermore, we identified 43 unreported Rank I ARG families that should be prioritized for public health interventions. Within the same gene family, homologous genes pose different risks, host range, and ecological distributions, indicating the need for high resolution surveillance into their sequence variants. Finally, to help strategize the policy interventions, we studied the impact of industrialization on high risk ARGs in 1,120 human gut microbiome metagenomes of 36 diverse global populations. Our findings suggest that current policies on controlling the clinical antimicrobial consumptions could effectively control Rank I, while greater antibiotic stewardship in veterinary settings could help control Rank II. Overall, our framework offered a straightforward evaluation of the risk posed by ARGs, and prioritized a shortlist of current and emerging threats for global action to fight ARGs.

microbiology↗

bioRxiv2019

24-hour multi-omics analysis of residential sewage reflects human activity and informs public health

High-throughput molecular analysis of sewage is a promising tool for precision public health. Here, we combine sewer network and demographic data to identify a residential catchment for sampling, and explore the potential of applying untargeted genomics and metabolomics to sewage to collect actionable public health data. We find that wastewater sampled upstream in a residential catchment is representative of the human microbiome and metabolome, and we are able to identify glucuronidated compounds indicative of direct human excretion, which are typically degraded too quickly to be detected at treatment plants. We show that diurnal variations during 24-hour sampling can be leveraged to discriminate between biomarkers in sewage that are associated with human activity from those related to the environmental background. Finally, we putatively annotate a suite of human-associated metabolites, including pharmaceuticals, food metabolites, and biomarkers of human health and activity, suggesting that mining untargeted data derived from residential sewage can expand currently-used biomarkers with direct public health or policy relevance.

genomics↗

bioRxiv2019

Progressive shifts in the gut microbiome reflect prediabetes and diabetes development in a treatment-naive Mexican cohort

Type 2 diabetes (T2D) is a global epidemic that affects more than 8% of the worlds population and is a leading cause of death in Mexico. Diet and lifestyle are known to contribute to the onset of T2D. However, the role of the gut microbiome in T2D progression remains uncertain. Associations between microbiome composition and diabetes are confounded by medication use, diet, and obesity. Here we present data on a treatment-naive cohort of 405 Mexican individuals across varying stages of T2D severity. Associations between gut bacteria and more than 200 clinical variables revealed a defined set of bacterial genera that were consistent biomarkers of T2D prevalence and risk. Specifically, gradual increases in blood glucose levels, beta cell dysfunction, and the accumulation of measured T2D risk factors was correlated with the relative abundances of four bacterial genera. In a cohort of 25 individuals, T2D treatment - predominantly metformin - reliably returned the microbiome to the normoglycemic community state. Deep clinical characterization allowed us to broadly control for confounding variables, indicating that these microbiome patterns were independent of common T2D comorbidities, like obesity or cardiovascular disease. Thus, our work provides the first solid evidence for a direct link between the gut microbiome and T2D in a critically high-risk population. Whether or not these T2D-associated changes in the gut contribute to the etiology of T2D or its comorbidities remains to be seen.

systems biology↗

bioRxiv2019

Dynamic colonization of microbes and their functions after fecal microbiota transplantation for inflammatory bowel disease

For fecal microbiota transplantation (FMT) to be successful in complex immune diseases like inflammatory bowel disease (IBD), it is assumed that therapeutic microbes and their beneficial functions and immune interactions must colonize the recipient and persist in sufficient quantity and for a long enough period of time to result in a clinical benefit. But few studies have comprehensively profiled the colonization and persistence of transferred microbes along with the transfer of their microbial and immune functions. Using 16S, metagenomic, and immunoglobulin A (IgA) sequencing, we analyzed hundreds of longitudinal microbiome samples from a randomized controlled trial of 12 patients with ulcerative colitis who received fecal transplant or placebo for 12 weeks. We uncovered a range of competitive dynamics among donor and patient strains, showing that persistence of transferred microbes is far from static. Indeed, one patient experienced dramatic loss of donor bacteria 10 weeks into the trial, coinciding with a bloom of pathogenic bacteria and worsening clinical symptoms. We similarly evaluated transfer of microbial functions, including desired ones like butyrate production and unintended ones like antibiotic resistance. By profiling bacteria coated with IgA, we identified IgA-coated bacteria associated with inflammation, and we found that microbial interactions with the host immune system can be transferred across people. This transfer of immune function is likely critical for gut microbiome therapeutics for immune-related diseases. Our findings elucidate the colonization dynamics of gut microbes as well as their functions in the context of FMT to treat a complex disease--information that may provide a critical foundation for the development of more-targeted therapeutics.

microbiology↗

bioRxiv2019

Multi-site sampling and risk prioritization reveals the public health relevance of antibiotic resistance genes found in wastewater environments

The spread of bacterial antibiotic resistance across human and environmental habitats is a global public health challenge. Wastewater has been implicated as a major source of antibiotic resistance in the environment, as it carries resistant bacteria and resistance genes from humans into natural ecosystems. However, different wastewater environments and antibiotic resistance genes in wastewater do not all present the same level of risk to human health. In this study, we investigate the public health relevance of antibiotic resistance found in wastewater by combining metagenomic sequencing with risk prioritization of resistance genes, analyzing samples across urban sewage system environments in multiple countries. We find that many of the resistance genes commonly found in wastewater are not readily present in humans. Ranking antibiotic resistance genes based on their potential pathogenicity and mobility reveals that most of the resistance genes in wastewater are not clinically relevant. Additionally, we show that residential wastewater resistomes pose greater risk to human health than those in wastewater treatment plant samples, and that residential wastewater can be as risky as hospital effluent. Across countries, differences in antibiotic resistance in residential wastewater can, in some cases, reflect differences in antibiotic drug consumption. Finally, we find that the flow of antibiotic resistance genes is influenced by geographical distance and environmental selection. Taken together, we demonstrate how different analytical approaches can provide greater insights into the public health relevance of antibiotic resistance in wastewater.

microbiology↗

bioRxiv2019

Transmission of human-associated microbiota along family and social networks

The human microbiome, described as an accessory organ because of the crucial functions it provides, is composed of species that are uniquely found in humans1,2. Yet, surprisingly little is known about the impact of routine interpersonal contacts in shaping microbiome composition. In a relatively closed cohort of 287 people from the Fiji Islands, where common barriers to bacterial transmission are absent, we examine putative bacterial transmission in individuals gut and oral microbiomes using strain-level data from both core SNPs and flexible genomic regions. We find a weak signal of transmission, defined by the inferred sharing of genotypes, across many organisms that, in aggregate, reveals strong transmission patterns, most notably within households and between spouses. We find that women harbor strains more closely related to those harbored by their familial and social contacts than men; and that transmission patterns of oral- and gut-associated microbiota need not be the same. Using strain-level data alone, we are able to confidently predict a subset of spouses, highlighting the role of shared susceptibilities, behaviors or social interactions that distinguish specific links in the social network.

microbiology↗

bioRxiv2019

Capturing the Diversity of Subsurface Microbiota - Choice of Carbon Source for Microcosm Enrichment and Isolation of Groundwater Bacteria

Improved and innovative enrichment/isolation techniques that yield to relevant isolates representing the true diversity of environmental microbial communities would significantly advance exploring the physiology of ecologically important taxa in ecosystems. Traditionally, either simple organic carbon (C) or yeast extract is used as C source in culture medium for microbial enrichment/isolation in laboratory. In natural environment, however, microbial population and evolution are greatly influenced by the property and composition of natural organic C. In this study, 8 types of organic C sources were fed to intrinsic groundwater microbes collected at Oak Ridge Reservation Field Research Center (ORR-FRC) background site for a 30-day incubation period to investigate the response of indigenous bacterial communities to different C sources. The tested C sources included simple organic C (glucose, acetate, benzoate, oleic acid, and cellulose) that are either traditionally used as C source in bacterial culture medium or present in natural environments; naturally occurring undefined complex C (bacterial cell lysate and sediment-derived natural organic matter (NOM)); as well as vitamin mixture which is a commonly used ingredient in culture medium. Our results clearly indicate that natural complex C substrates served better in enriching diverse bacteria compared to other C sources. Microcosms amended with small organic C (glucose, acetate, benzoate, or oleic acid) showed significantly lower biodiversity than control groups, dominated by only a few phyla of bacteria such as Proteobacteria and Bacteroidetes which are commonly isolated and already have diverse representative isolates, while those amended with natural complex C (cell lysate or NOM) displayed significantly higher biodiversity than control groups, in which three phyla (Verrucomicrobia, Planctomycetes, and Armatimonadetes) that are poorly represented in published culture collections were abundantly enriched. Further isolation of pure bacterial strains from complex C-amended enrichments led to 51 species representing 4 phyla, 13 orders. Furthermore, 5 isolates with low similarities to published strains were considered to be novel. Results from this study will aid in the design of better cultivation and isolation strategy for maximize the diversity of organisms recovered from subsurface environment.

microbiology↗

bioRxiv2018

A practical guide to methods controlling false discoveries in computational biology

BackgroundIn high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p-values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as \"informative covariates\" to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigated the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology\n\nResultsMethods that incorporate informative covariates were modestly more powerful than classic approaches, and did not underperform classic approaches, even when the covariate was completely uninformative. The majority of methods were successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we found the improvement of the modern FDR methods over the classic methods increased with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses.\n\nConclusionsModern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.

bioinformatics↗

bioRxiv2018

Interactions in self-assembled microbial communities saturate with diversity

How the diversity of organisms competing for or sharing resources influences community production is an important question in ecology but has rarely been explored in natural microbial communities. These generally contain large numbers of species making it difficult to disentangle how the effects of different interactions scale with diversity. Here, we show that changing diversity affects measures of community function in relatively simple communities but that increasing richness beyond a threshold has little detectable effect. We generated self-assembled communities with a wide range of diversity by growth of cells from serially diluted seawater on brown algal leachate. We subsequently isolated the most abundant taxa from these communities via dilution-to-extinction in order to compare productivity functions of the entire community to those of individual taxa. To parse the effect of different types of organismal interactions, we developed relative total function (RTF) as an index for positive or negative effects of diversity on community function. Our analysis identified three overall regimes with increasing diversity. At low richness (<12 taxa), potential positive and negative effects of interactions are both weak, while at moderate richness (12-20 taxa), community resource uptake increases but the carbon use efficiency decreases. Finally, beyond 20 taxa, there was no net change in community function indicating a saturation of potential interactions. These data suggest that although more diverse communities had overall greater access to resources, individual taxa within these communities had lower resource availability and reduced carbon use efficiency, indicating that competition due to niche overlap increases with diversity but that these interactions saturate at a specific threshold.

ecology↗

bioRxiv2018

Longitudinal immunosequencing in healthy people reveals persistent T cell receptors rich in public receptors

BackgroundThe adaptive immune system maintains a diversity of T cells capable of recognizing a broad array of antigens. Each T cells specificity and affinity for antigens is determined by its T cell receptors (TCRs), which together across all T cells form a repertoire of tens of millions of unique receptors in each individual. Although many studies have examined how TCR repertoires change in response to disease or drugs, few have explored the temporal dynamics of the TCR repertoire in healthy individuals.\n\nResultsHere we report immunosequencing of TCR {beta} chains (TCR{beta}) from the blood of three healthy individuals at eight time points over one year. TCR{beta} repertoires from samples of all T cells and memory T cells clearly clustered by individual, confirming that TCR{beta} repertoires are specific to individuals across time. This individuality was absent from TCR{beta}s from naive T cells, suggesting that these differences result from an individuals antigen exposure history. Many characteristics of the TCR{beta} repertoire (e.g., alpha diversity, clonality) were stable across time, although we found evidence of T cell expansion dynamics even within healthy individuals. We further identified a subset of \"persistent\" TCR{beta}s present across all time points, and these receptors were rich in clonal and public receptors.\n\nConclusionsOur results revealed persistent receptors that may play a key role in immune system maintenance. They further highlight the importance of longitudinal sampling of the immune system and provide a much-needed baseline for TCR{beta} dynamics in healthy individuals. Such a baseline should help improve interpretation of changes in the TCR{beta} repertoire during disease or treatment.

immunology↗

bioRxiv2017

Endospores and other lysis-resistant bacteria comprise a widely shared core community within the human microbiota

Endospore-formers in the human microbiota are well adapted for host-to-host transmission, and an emerging consensus points to their role in determining health and disease states in the gut. The human gut, more than any other environment, encourages the maintenance of endospore formation, with recent culture-based work suggesting that over 50% of genera in the microbiome carry genes attributed to this trait. However, there has been limited work on the ecological role of endospores and other stress-resistant cellular states in the human gut. In fact, there is no data to indicate whether organisms with the genetic potential to form endospores actually form endospores in situ and how sporulation varies across individuals and over time. Here, we applied a culture-independent protocol to enrich for endospores and other stress-resistant cells in human feces to identify variation in these states across people and within an individual over time. We see that cells with resistant states are more likely than those without to be shared among multiple individuals, which suggests that these resistant states are particularly adapted for cross-host dissemination. Furthermore, we use untargeted fecal metabolomics in 24 individuals and within a person over time to show that these organisms respond to shared environmental signals, and in particular, dietary fatty acids, that likely mediate colonization of recently disturbed human guts.

microbiology↗

bioRxiv2017

Comprehensive analysis of mobile genetic elements in the gut microbiome reveals a phylum-level niche-adaptive gene pool

Mobile genetic elements (MGEs) drive extensive horizontal transfer in the gut microbiome. This transfer could benefit human health by conferring new metabolic capabilities to commensal microbes, or it could threaten human health by spreading antibiotic resistance genes to pathogens. Despite their biological importance and medical relevance, MGEs from the gut microbiome have not been systematically characterized. Here, we present a comprehensive analysis of chromosomal MGEs in the gut microbiome using a method called Split Read Insertion Detection (SRID) that enables the identification of the exact mobilizable unit of MGEs. Leveraging the SRID method, we curated a database of 5600 putative MGEs encompassing seven MGE classes called ImmeDB (Intestinal microbiome mobile element database) (https://immedb.mit.edu/). We observed that many MGEs carry genes that confer an adaptive advantage to the gut environment including gene families involved in antibiotic resistance, bile salt detoxification, mucus degradation, capsular polysaccharide biosynthesis, polysaccharide utilization, and sporulation. We find that antibiotic resistance genes are more likely to be spread by conjugation via integrative conjugative elements or integrative mobilizable elements than transduction via prophages. Additionally, we observed that horizontal transfer of MGEs is extensive within phyla but rare across phyla. Taken together, our findings support a phylum level niche-adaptive gene pools in the gut microbiome. ImmeDB will be a valuable resource for future fundamental and translational studies on the gut microbiome and MGE communities.

bioinformatics↗

bioRxiv2017

Adaptive evolution within the gut microbiome of individual people

Individual bacterial lineages stably persist for years in the human gut microbiome1-3. However, the potential of these lineages to adapt during colonization of healthy people is not well understood2,4. Here, we assess evolution within individual microbiomes by sequencing the genomes of 602 Bacteroides fragilis isolates cultured from 12 healthy subjects. We find that B. fragilis within-subject populations contain substantial de novo nucleotide and mobile element diversity, which preserve years of within-person evolutionary history. This evolutionary history contains signatures of within-person adaptation to both subject-specific and common selective forces, including parallel mutations in sixteen genes. These sixteen genes are involved in cell-envelope biosynthesis and polysaccharide utilization, as well as yet under-characterized pathways. Notably, one of these genes has been shown to be critical for B. fragilis colonization in mice5, indicating that key genes have not already been optimized for survival in vivo. This lack of optimization, given historical signatures of purifying selection in these genes, suggests that varying selective forces with discordant solutions act upon B. fragilis in vivo. Remarkably, in one subject, two B. fragilis sublineages coexisted at a stable relative frequency over a 1.5-year period despite rapid adaptive dynamics within one of the sublineages. This stable coexistence suggests that competing selective forces can lead to B. fragilis niche-differentiation even within a single person. We conclude that B. fragilis adapts rapidly within the microbiomes of individual healthy people, providing a new route for the discovery of key genes in the microbiome and implications for microbiome stability and manipulation.

evolutionary biology↗

bioRxiv2017

Correcting for batch effects in case-control microbiome studies

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.\n\nAuthor SummaryBatch effects are obstacles to comparing results across studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fishers method, are effective but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control microbiome datasets.

bioinformatics↗

bioRxiv2017

Random Sequences Rapidly Evolve Into De Novo Promoters

How do new promoters evolve? To follow evolution of de novo promoters, we put various random sequences upstream to the lac operon in Escherichia coli and evolved the cells in the presence of lactose. We found that a typical random sequence of ~100 bases requires only one mutation in order to enable growth on lactose by increasing resemblance to the canonical promoter motifs. We further found that ~10% of random sequences could serve as active promoters even without any period of evolutionary adaptation. Such a short mutational distance from a random sequence to an active promoter may improve evolvability yet may also lead to undesirable accidental expression. We found that across the E. coli genome accidental expression is minimized by avoiding codon combinations that resemble promoter motifs. Our results suggest that the promoter recognition machinery has been tuned to allow high accessibility to new promoters, and similar findings might also be observed in higher organisms or in other motif recognition machineries, like transcription factor binding sites or protein-protein interactions.

molecular biology↗

bioRxiv2017

Meta Analysis Of Microbiome Studies Identifies Shared And Disease-Specific Patterns

1Hundreds of clinical studies have been published that demonstrate associations between the human microbiome and a variety of diseases. Yet, fundamental questions remain on how we can generalize this knowledge. For example, if diseases are mainly characterized by a small number of pathogenic species, then new targeted antimicrobial therapies may be called for. Alternatively, if diseases are characterized by a lack of healthy commensal bacteria, then new probiotic therapies might be a better option. Results from individual studies, however, can be inconsistent or in conflict, and comparing published data is further complicated by the lack of standard processing and analysis methods.\n\nHere, we introduce the MicrobiomeHD database, which includes 29 published case-control gut microbiome studies spanning ten different diseases. Using standardized data processing and analyses, we perform a comprehensive crossdisease meta-analysis of these studies. We find consistent and specific patterns of disease-associated microbiome changes. A few diseases are associated with many individual bacterial associations, while most show only around 20 genus-level changes. Some diseases are marked by the presence of pathogenic microbes whereas others are characterized by a depletion of health-associated bacteria. Furthermore, over 60% of microbes associated with individual diseases fall into a set of \"core\" health and disease-associated microbes, which are associated with multiple disease states. This suggests a universal microbial response to disease.

microbiology↗